PROVIDED. Cancer surveillance plays an essential role in cancer prevention and intervention. This proposal develops new statistical methods that deal with complex data-related issues in cancer surveillance studies. In particular, the specific aims are motivated by problems encountered in surveillance studies that monitor cancer mortality and geographical patterns, and that study disproportionate disease burden on particular populations and important risk factors. We plan to (1) develop new methods to analyze the cross-relationship matrix of the change trends [e.g. the annual rate changes (ARC)] in mortality or incidence on multiple cancer sites for the period of 1969-2004; (2) propose disease clustering/surveillance methods for outcomes subject to censoring; (3) propose a new test statistic for spatial clustering detection that incorporates latency distributions that are associated with cancer, and studies whether disease clustering patterns differ according to genetic characteristics; (4) develop and evaluate a spatio-temporal hidden Markov model for disease surveillance based on regionspecific counts of disease incidence; (5) develop efficient algorithms and user-friendly statistical software that implement these methods with the goal of disseminating them to health science researchers. The proposed methods will be applied to several cancer and environmental health projects that the investigators have been involved in, namely, the SEER cancer mortality data, the SEER prostate cancer incidence data and the Taiwan Leukemia data. The methods will allow practitioners as well as health care policy makers to better understand the change trends of cancer deaths/incidence and the cross-relationship of these trends for the purpose of planning and resource allocation. The methods will also help reveal disproportionate disease burden on at-risk populations and identify important risk factors, including genetic susceptibility. The surveillance methods proposed in this project are linked to the spatio-temporal methods proposed in Project 1, and the regularized regression models proposed in this project are related to the variable selection methods proposed in Project 3. In addition, all three projects have a common theme of the analysis of high-dimensional observational study data, and all projects will generate statistical methods and computational approaches that will inform those developed in the others.